Field of the Disclosure
The present disclosure relates to a technique for searching for a registered image similar to a query image.
Description of the Related Art
A method for searching for a similar image using a local feature amount of an image has been discussed. According to such a method, a feature point (a local feature point) is extracted from an image (“A combined corner and edge detector”, by C. Harris, and M. J. Stephens, In Alvey Vision Conference, pp. 147-152 (1988)). Then, a feature amount (a local feature amount) corresponding to the local feature point is calculated based on the local feature point and image information about the vicinity of the local feature point (“Distinctive Image Features from Scale-Invariant Keypoints”, by David G. Lowe, International Journal of Computer Vision, 60, 2. pp. 91-110 (2004)).
In the method using the local feature amount, the local feature amount is defined as information including a scale-invariant. This enables an image to be searched even if the image is rotated, enlarged, or reduced. In general, the local feature amount is expressed as a vector. Theoretically, the local feature amount rotation-invariant and scale-invariant. However, in an actual digital image, local feature amounts acquired before a rotation, enlargement, or reduction process is performed on the image and corresponding local feature amounts acquired after the rotation, enlargement, or reduction process is performed on the image slightly differ.
For extraction of a rotation-invariant local feature amount discussed in, for example, “Distinctive Image Features from Scale-Invariant Keypoints”, by David G. Lowe, International journal of Computer Vision, 60, 2, pp. 91-110 (2004), a main direction is calculated from a pixel pattern of a local area in the vicinity of the local feature point, and a direction is normalized by rotating the local area using the main direction as a reference when a local feature amount is calculated. Moreover, different scale images are internally generated for calculation of a scale-invariant local feature amount, and extraction of a local feature point and calculation of the local feature amount are performed from each of the different scale images. A collection of the internally generated different scale images is generally called a scale space.
According to the above method, a plurality of local feature points is extracted from an image of one sheet. In the image search using the local feature amount, local feature amounts calculated from the respective local feature points are compared to undergo matching. A widely used voting method (Japanese Patent Application Laid-Open No. 2009-284084) performs a nearest neighbor process to determine whether there is a feature point that is similar to a local feature amount of each of feature points extracted from a query image by a predetermined threshold value or greater. If there such a feature point, one vote is cast with respect to the “image”. The larger the number of votes, the more similarity.
The technique discussed in Japanese Patent Application Laid-Open No. 2009-284084 searches for an image by using image shape similarity and color similarity. Thus, for example, in a case where a plurality of images in which only one portion of each image is changed is registered in a database, the plurality of images can be acquired as search results. That is, such a plurality of images has higher similarity than other images. A large portion of each of the plurality of images is the same, and only a small portion differs. This causes difficulty in generating a difference in similarity of the plurality of images. Consequently, there are cases where an image similar to a query image cannot be identified from the plurality of images. Accordingly, there a need to enhance search accuracy at the time of search of a registered image similar to a query image.